Detecting propagandistic poster title: a machine learning approach

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Manylion Llyfryddiaeth
Prif Awduron:	Mahmood, Riaz, Shah, Intiajul Alam, Hassan, Tasnimul, Abdullah, Hasan, Mubassir, Taskin Mohammad
Awduron Eraill:	Alam, Md. Golam Rabiul
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	Brac University 2024
Pynciau:	Misinformation Propaganda identification Machine learning models Societal peacekeeping Machine learning. Artificial intelligence. Image processing > Data mining.
Mynediad Ar-lein:	http://hdl.handle.net/10361/24152

id	10361-24152
record_format	dspace
spelling	10361-241522024-09-25T05:42:53Z Detecting propagandistic poster title: a machine learning approach Mahmood, Riaz Shah, Intiajul Alam Hassan, Tasnimul Abdullah, Hasan Mubassir, Taskin Mohammad Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Misinformation Propaganda identification Machine learning models Societal peacekeeping Machine learning. Artificial intelligence. Image processing--Data mining. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages no. 31-32). Detecting propagandistic content is crucial in today’s digital age where misinformation spreads rapidly. In this study, we propose a machine learning approach aimed at identifying propaganda in poster titles. Our methodology encompasses various text classification techniques, including Random Forest, Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes classifier, Support Vector Machine (SVM), RoBERTa, Stacking Classifier, Stacking Classifier With Feature Engineering, and RoBERTa XGBoost Hybrid Model. We employ robust feature extraction methods such as TF-IDF and Word2Vec, along with advanced ensemble learning strategies, to enhance the accuracy and effectiveness of the classification process. Specifically, we introduce two hybrid models: the Stacking Classifier With Feature Engineering, which incorporates word2vec and TF-IDF to improve accuracy, and the RoBERTa XGBoost Hybrid Model, which utilizes a combination of TF-IDF vectorization and RoBERTa embeddings followed by XGBoost classification. Through extensive experimentation and evaluation, we analyze the performance of each model in terms of accuracy, precision, recall, and F1-score. Our findings demonstrate promising results, with certain models exhibiting significant improvements over baseline approaches. Moreover, we conduct a thorough analysis of the models’ strengths and weaknesses, providing insights into their efficacy in detecting propagandistic content. Overall, our research contributes to the development of effective tools for combating propagandistic title and promoting media literacy in the digital landscape. Riaz Mahmood Intiajul Alam Shah Tasnimul Hassan Hasan Abdullah Taskin Mohammad Mubassir B.Sc. in Computer Science 2024-09-22T05:27:07Z 2024-09-22T05:27:07Z ©2024 2024-03 Thesis ID 19201007 ID 19301185 ID 19341001 ID 19301247 ID 19201114 http://hdl.handle.net/10361/24152 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 41 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Misinformation Propaganda identification Machine learning models Societal peacekeeping Machine learning. Artificial intelligence. Image processing--Data mining.
spellingShingle	Misinformation Propaganda identification Machine learning models Societal peacekeeping Machine learning. Artificial intelligence. Image processing--Data mining. Mahmood, Riaz Shah, Intiajul Alam Hassan, Tasnimul Abdullah, Hasan Mubassir, Taskin Mohammad Detecting propagandistic poster title: a machine learning approach
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Mahmood, Riaz Shah, Intiajul Alam Hassan, Tasnimul Abdullah, Hasan Mubassir, Taskin Mohammad
format	Thesis
author	Mahmood, Riaz Shah, Intiajul Alam Hassan, Tasnimul Abdullah, Hasan Mubassir, Taskin Mohammad
author_sort	Mahmood, Riaz
title	Detecting propagandistic poster title: a machine learning approach
title_short	Detecting propagandistic poster title: a machine learning approach
title_full	Detecting propagandistic poster title: a machine learning approach
title_fullStr	Detecting propagandistic poster title: a machine learning approach
title_full_unstemmed	Detecting propagandistic poster title: a machine learning approach
title_sort	detecting propagandistic poster title: a machine learning approach
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24152
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_version_	1814309821326819328

Detecting propagandistic poster title: a machine learning approach

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